code: def inference(self, x_all, subgraph_loader, device):
      pbar = tqdm.tqdm(total=x_all.size(0) * len(self.convs))
      pbar.set_description("Evaluating")
  
      errs = []
      x_all = x_all.cpu()
      for i, conv in enumerate(self.convs):
          xs = []
          for batch_size, n_id, adj in subgraph_loader:
              edge_index, _, size = adj.to(device)
              x = x_all[n_id].to(device)
              h = conv(x, edge_index)
              if i != len(self.convs) - 1:
                  if self.batch_norm:
                      h = self.bns[i](h)
                  if self.residual and h.size(-1) == x.size(-1):
                      h += x[: h.size(0)]
                  x = F.relu(h)
                  x = F.dropout(
                      x, p=self.dropout, training=self.training
                  )
              else:
                  x = h
              xs.append(x.cpu())
              pbar.update(batch_size)
  
          x_all = torch.cat(xs, dim=0)
          if i != len(self.convs) - 1:
              err = (
                  torch.norm((self.histories[i].emb - x_all), dim=1)
                  .mean()
                  .item()
              )
              errs.append(err)
              print(
                  f"The mean error of the history embedding on layer {i} is {err:.4f}."
              )
  
      pbar.close()
      return errs, x_all
  
imports: from itertools import product
  from typing import Optional, List
  import tqdm
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d
  from torch_sparse import SparseTensor
  from torch_geometric.nn import MessagePassing
  from torch_geometric.nn import PNAConv
  from torch_scatter import segment_csr
  from ..models import ScalableGNN
label_desc: Use image node_id 7 for calling the PNA obj's underlying member method code with example usage: obj.inference(x_all, subgraph_loader, device) and returns: errs,  x_all